plot_two_compartment_clone_sizes_variability.py

"""Different clone size distributions, short term. Comparing variability."""
import numpy
from matplotlib import pyplot as plt

from stem_cell_model import clone_size_simulator
from stem_cell_model.clone_size_simulator import CloneSizeSimulationConfig
from stem_cell_model.parameters import SimulationParameters, SimulationConfig
from stem_cell_model.two_compartment_model_space import run_simulation_niche

D = 30
T = (16.153070175438597, 3.2357834505600382)  # Based on measured values

parameters_symmetric_low_noise = SimulationParameters.for_D_alpha_and_phi(
    D=D, alpha_n=0.95, alpha_m=-0.95, phi=0.95, T=T, a=1/T[0])
parameters_symmetric_high_noise = SimulationParameters.for_D_alpha_and_phi(
    D=D, alpha_n=0.05, alpha_m=-0.05, phi=0.95, T=T, a=1/T[0])

random = numpy.random.Generator(numpy.random.MT19937(seed=1))
t_clone_size = 60
config = CloneSizeSimulationConfig(t_clone_size=t_clone_size, random=random, n_crypts=1000)

fig, (ax_left, ax_right) = plt.subplots(1, 2)

results_symmetric_low_noise = clone_size_simulator.calculate(
    run_simulation_niche, config, parameters_symmetric_low_noise)
ax_left.set_title("Low noise ($\\alpha_n = 0.95$)")
ax_left.bar(results_symmetric_low_noise.indices(), results_symmetric_low_noise.to_height_array())
ax_left.set_xticks([i for i in results_symmetric_low_noise.indices() if i % 2 == 0])
ax_left.set_xlabel("Clone size")

results_symmetric_high_noise = clone_size_simulator.calculate(
    run_simulation_niche, config, parameters_symmetric_high_noise)
ax_right.set_title("High noise ($\\alpha_n = 0.05$)")
ax_right.bar(results_symmetric_high_noise.indices(), results_symmetric_high_noise.to_height_array())
ax_right.set_xticks([i for i in results_symmetric_high_noise.indices() if i % 2 == 0])


print("Average of high noise:", results_symmetric_high_noise.get_average_and_st_dev())
print("Average of low noise:", results_symmetric_low_noise.get_average_and_st_dev())

plt.show()